1 Introduction

This paper contains estimates for the effective reproduction number \(R_{t,m}\) over time \(t\) in various countries \(m\) of the world. This is done using the methodology as described in [1]. These have been implemented in R using EpiEstim package [2] which is what is used here. The methodology and assumptions are described in more detail here.

This paper and it’s results should be updated roughly daily and is available online.

As this paper is updated over time this section will summarise significant changes. The code producing this paper is tracked using Git. The Git commit hash for this project at the time of generating this paper was f221631e6bbf1f1648d190ad5237e344a4bf5585.

2 Data

Data are downloaded from [3]. Minor formatting is applied to get the data ready for further processing.

3 Basic Exploration

Below we plot cumulative case count on a log scale by continent. Note that “Other” relates to ships.

Reported Cases by Continent

Reported Cases by Continent

Below we plot the cumulative deaths by country on a log scale:

Reported Deaths by Continent

Reported Deaths by Continent

4 Method & Assumptions

The methodology is described in detail here.

Countries with populations of less than 500 000 are excluded.

5 Results

5.1 World-wide

Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
cases 2,865,245 2021-03-10 1.1 1.1 1.1
deaths 59,444 2021-03-10 0.9 1.0 1.0

5.2 Current reproduction number estimates by country

Below current (last weekly) \(R_{t,m}\) estimates are plotted on a world map.

5.2.0.1 Cases

5.2.1 Deaths

5.3 Top 10 countries

Below we show various extremes of \(R_{t,m}\) where counts (deaths or cases) exceed 50 in the last week.

5.3.1 Lowest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
United Kingdom deaths 1,205 2021-03-10 0.6 0.7 0.7
Ecuador deaths 184 2021-03-10 0.6 0.7 0.8
Sweden deaths 124 2021-03-10 0.6 0.7 0.8
Portugal deaths 187 2021-03-10 0.6 0.7 0.8
Honduras deaths 87 2021-03-10 0.6 0.7 0.9
Philippines deaths 156 2021-03-10 0.6 0.8 0.9
Germany deaths 1,573 2021-03-10 0.7 0.8 0.8
United States deaths 10,058 2021-03-10 0.8 0.8 0.8
Bolivia deaths 150 2021-03-10 0.7 0.8 1.0
North America deaths 15,200 2021-03-10 0.8 0.8 0.9

5.3.2 Lowest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Haiti cases 63 2021-03-10 0.3 0.4 0.6
Equatorial Guinea cases 161 2021-03-10 0.5 0.5 0.6
Eswatini cases 100 2021-03-10 0.4 0.5 0.7
Malawi cases 385 2021-03-10 0.5 0.5 0.6
South Sudan cases 565 2021-03-10 0.5 0.6 0.6
Democratic Republic of Congo cases 491 2021-03-10 0.5 0.6 0.7
Myanmar cases 89 2021-03-10 0.5 0.6 0.8
Botswana cases 2,185 2021-03-10 0.6 0.7 0.8
Lithuania cases 3,035 2021-03-10 0.6 0.7 0.8
Kyrgyzstan cases 230 2021-03-10 0.6 0.7 0.8

5.3.3 Highest \(R_{t,m}\) based on deaths

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Botswana deaths 81 2021-03-10 1.3 1.7 2.1
Libya deaths 111 2021-03-10 1.2 1.5 1.8
North Macedonia deaths 88 2021-03-10 1.2 1.5 1.8
Jordan deaths 313 2021-03-10 1.3 1.5 1.7
Uruguay deaths 54 2021-03-10 1.1 1.4 1.9
Bosnia and Herzegovina deaths 208 2021-03-10 1.2 1.4 1.7
Bulgaria deaths 586 2021-03-10 1.3 1.4 1.5
Kosovo deaths 54 2021-03-10 1.0 1.3 1.7
Bangladesh deaths 68 2021-03-10 1.0 1.3 1.7
Palestine deaths 115 2021-03-10 1.1 1.3 1.6

5.3.4 Highest \(R_{t,m}\) based on cases

Country Estimated Type Count (Last Week) Week Ending R - Lower CI R - Mean R - Uppper CI
Niger cases 108 2021-03-10 2.5 9.9 41.1
Madagascar cases 324 2021-03-10 1.5 2.6 7.2
Cameroon cases 1,080 2021-03-10 1.3 2.3 5.8
Djibouti cases 125 2021-03-10 1.6 2.0 2.4
Jamaica cases 4,170 2021-03-10 1.6 1.7 1.9
Mali cases 264 2021-03-10 1.4 1.7 2.0
Yemen cases 264 2021-03-10 1.4 1.7 2.0
Congo cases 509 2021-03-10 1.5 1.7 1.9
Papua New Guinea cases 376 2021-03-10 1.3 1.5 1.8
Cote d’Ivoire cases 2,743 2021-03-10 1.3 1.5 1.8

5.4 Risk Quadrants

The plots below show weekly cases (or deaths) on the X-axis and the reproduction number on the Y-axis. By dividing this into 4 quadrants we can identify countries with high cases and high reproduction numbers, or high cases and low reproduction numbers etc.

Values where the reproduction number exceeds 3 are plotted at 3.

5.4.1 Cases

Risk Quadrants - Cases

5.4.2 Deaths

Risk Quadrants - Deaths

5.5 Country Plots by Continent

Below we plot results for each country/province in a list. Values larger than 3 are plotted at 3.

5.5.1 Africa

5.5.1.1 Algeria

5.5.1.2 Angola

5.5.1.3 Benin

5.5.1.4 Botswana

5.5.1.5 Burkina Faso

5.5.1.6 Burundi

5.5.1.7 Cameroon

5.5.1.8 Cape Verde

5.5.1.9 Central African Republic

5.5.1.10 Chad

5.5.1.11 Comoros

5.5.1.12 Congo

5.5.1.13 Cote d’Ivoire

5.5.1.14 Democratic Republic of Congo

5.5.1.15 Djibouti

5.5.1.16 Egypt

5.5.1.17 Equatorial Guinea

5.5.1.18 Eritrea

5.5.1.19 Eswatini

5.5.1.20 Ethiopia

5.5.1.21 Gabon

5.5.1.22 Gambia

5.5.1.23 Ghana

5.5.1.24 Guinea

5.5.1.25 Guinea-Bissau

5.5.1.26 Kenya

5.5.1.27 Lesotho

5.5.1.28 Liberia

5.5.1.29 Libya

5.5.1.30 Madagascar

5.5.1.31 Malawi

5.5.1.32 Mali

5.5.1.33 Mauritania

5.5.1.34 Mauritius

5.5.1.35 Morocco

5.5.1.36 Mozambique

5.5.1.37 Namibia

5.5.1.38 Niger

5.5.1.39 Nigeria

5.5.1.40 Rwanda

5.5.1.41 Senegal

5.5.1.42 Sierra Leone

5.5.1.43 Somalia

5.5.1.44 South Africa

5.5.1.45 South Sudan

5.5.1.46 Sudan

5.5.1.47 Togo

5.5.1.48 Tunisia

5.5.1.49 Uganda

5.5.1.50 Zambia

5.5.1.51 Zimbabwe

5.5.2 Asia

5.5.2.1 Afghanistan

5.5.2.2 Armenia

5.5.2.3 Azerbaijan

5.5.2.4 Bahrain

5.5.2.5 Bangladesh

5.5.2.6 Bhutan

5.5.2.7 Cambodia

5.5.2.8 China

5.5.2.9 Georgia

5.5.2.10 India

5.5.2.11 Indonesia

5.5.2.12 Iran

5.5.2.13 Iraq

5.5.2.14 Israel

5.5.2.15 Japan

5.5.2.16 Jordan

5.5.2.17 Kazakhstan

5.5.2.18 Kuwait

5.5.2.19 Kyrgyzstan

5.5.2.20 Lebanon

5.5.2.21 Malaysia

5.5.2.22 Maldives

5.5.2.23 Mongolia

5.5.2.24 Myanmar

5.5.2.25 Nepal

5.5.2.26 Oman

5.5.2.27 Pakistan

5.5.2.28 Palestine

5.5.2.29 Philippines

5.5.2.30 Qatar

5.5.2.31 Saudi Arabia

5.5.2.32 Singapore

5.5.2.33 South Korea

5.5.2.34 Sri Lanka

5.5.2.35 Syria

5.5.2.36 Taiwan

5.5.2.37 Tajikistan

5.5.2.38 Thailand

5.5.2.39 Turkey

5.5.2.40 United Arab Emirates

5.5.2.41 Uzbekistan

5.5.2.42 Vietnam

5.5.2.43 Yemen

5.5.3 Europe

5.5.3.1 Albania

5.5.3.2 Austria

5.5.3.3 Belarus

5.5.3.4 Belgium

5.5.3.5 Bosnia and Herzegovina

5.5.3.6 Bulgaria

5.5.3.7 Croatia

5.5.3.8 Cyprus

5.5.3.9 Czechia

5.5.3.10 Denmark

5.5.3.11 Estonia

5.5.3.12 Finland

5.5.3.13 France

5.5.3.14 Germany

5.5.3.15 Greece

5.5.3.16 Hungary

5.5.3.17 Ireland

5.5.3.18 Italy

5.5.3.19 Kosovo

5.5.3.20 Latvia

5.5.3.21 Lithuania

5.5.3.22 Luxembourg

5.5.3.23 Moldova

5.5.3.24 Montenegro

5.5.3.25 Netherlands

5.5.3.26 North Macedonia

5.5.3.27 Norway

5.5.3.28 Poland

5.5.3.29 Portugal

5.5.3.30 Romania

5.5.3.31 Russia

5.5.3.32 Serbia

5.5.3.33 Slovakia

5.5.3.34 Slovenia

5.5.3.35 Spain

5.5.3.36 Sweden

5.5.3.37 Switzerland

5.5.3.38 Ukraine

5.5.3.39 United Kingdom

5.5.4 North America

5.5.4.1 Canada

5.5.4.2 Costa Rica

5.5.4.3 Cuba

5.5.4.4 Dominican Republic

5.5.4.5 El Salvador

5.5.4.6 Guatemala

5.5.4.7 Haiti

5.5.4.8 Honduras

5.5.4.9 Jamaica

5.5.4.10 Mexico

5.5.4.11 Nicaragua

5.5.4.12 Panama

5.5.4.13 Trinidad and Tobago

5.5.4.14 United States

5.5.5 Oceania

5.5.5.1 Australia

5.5.5.2 New Zealand

5.5.5.3 Papua New Guinea

5.5.6 South America

5.5.6.1 Argentina

5.5.6.2 Bolivia

5.5.6.3 Brazil

5.5.6.4 Chile

5.5.6.5 Colombia

5.5.6.6 Ecuador

5.5.6.7 Guyana

5.5.6.8 Paraguay

5.5.6.9 Peru

5.5.6.10 Suriname

5.5.6.11 Uruguay

5.5.6.12 Venezuela

5.6 Detailed Output

Detailed output for all countries are saved to a comma-separated value file. The file can be found here.

6 Discussion

Limitation of this method to estimate \(R_{t,m}\) are noted in [1]

  • It’s sensitive to changes in transmissibility, changes in contact patterns, depletion of the susceptible population and control measures.
  • It relies on an assumed generation interval assumptions.
  • The size of the time window can affect the volatility of results.
  • Results are time lagged with regards to true infection, more so in the case of the use of deaths.
  • It’s sensitive to changes in case (or death) detection.
  • The generation interval may change over time.

Further to the above the estimates are made under assumption that the cases and deaths are reported consistently over time. For cases this means that testing needs to be at similar levels and reported with similar lag. Should these change rapidly over an interval of a few weeks the above estimates of the effective reproduction numbers would be biased. For example a rapid expansion of testing over the last 3 weeks would results in overestimating recent effective reproduction numbers. Similarly any changes in reporting (over time and underreporting) of deaths would also bias estimates of the reproduction number estimated using deaths.

Estimates for the reproduction number are plotted in time period in which the relevant measure is recorded. Though in reality the infections giving rise to those estimates would have occurred roughly between a week to 4 weeks earlier depending on whether it was cases or deaths. These figures have not been shifted back.

Despite these limitation we believe the ease of calculation of this method and the ability to use multiple sources makes it useful as a monitoring tool.

7 Author

This report was prepared by Louis Rossouw. Please get in contact with Louis Rossouw if you have comments or wish to receive this regularly.

Louis Rossouw
Head of Research & Analytics
Gen Re | Life/Health Canada, South Africa, Australia, NZ, UK & Ireland
Email: LRossouw@GenRe.com Mobile: +27 71 355 2550

The views in this document represents that of the author and may not represent those of Gen Re. Also note that given the significant uncertainty involved with the parameters, data and methodology care should be taken with these numbers and any use of these numbers.

References

[1] A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, “A new framework and software to estimate time-varying reproduction numbers during epidemics,” American Journal of Epidemiology, vol. 178, no. 9, pp. 1505–1512, Sep. 2013, doi: 10.1093/aje/kwt133. [Online]. Available: https://doi.org/10.1093/aje/kwt133

[2] A. Cori, EpiEstim: A package to estimate time varying reproduction numbers from epidemic curves. 2013 [Online]. Available: https://CRAN.R-project.org/package=EpiEstim

[3] M. Roser, H. Ritchie, E. Ortiz-Ospina, and J. Hasell, “Coronavirus pandemic (COVID-19),” Our World in Data, 2020 [Online]. Available: https://ourworldindata.org/coronavirus. [Accessed: 17-Dec-2020]